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Hierarchical decision approach: Key to activated sludge process redesign
European Symposium on Computer Aided Process Engineering - 15 L. Puigjaner and A. Espuna (Editors) © 2005 Elsevier B.V. All rights reserved.
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Hierarchical Decision Approach: Key to Activated Sludge Process Redesign Xavier Flores^, Manel Poch^, Ignasi Rodriguez-Roda^, Rene Banares-Alcantara^ and Laureano Jimenez'^ ^Laboratori d'Enginyeria Quimica i Ambiental, Universitat de Girona Campus Montilivi s/n. 17071 Girona, Spain ^Department of Engineering Science, University of Oxford Parks Roads, Oxford 0X1 3PJ, U.K. '^Departament d'Enginyeria Quimica i MetaMurgica, Universitat de Barcelona Marti i Franques 1, 08028 Barcelona, Spain
Abstract Hierarchical Decision Process has been applied to redesign a wastewater treatment plant to achieve phosphorus removal The Hierarchical Decision Process suggests the order in which the decisions should be taken during the development of the new process, starting with the design of the reactor due to the strong interaction with the rest of the process. The redesign task is formulated as a multicriteria problem and considers simultaneously environmental, economical, legal and technical aspects. Finally, a sensitivity analysis is made to test the robustness and support the decision making procedure. Keywords: Activated sludge. Conceptual design. Nutrient removal. Hierarchical decision process, Multicriteria analysis.
1. Introduction The application of the European Directive (91/271/EEC) for urban wastewater treatment in sensitive areas requires a higher efficiency in nutrient removal from activated sludge plants. To achieve the strict phosphorus and nitrogen discharge limits implies a redesign of several units. Literature describes different options to enhance activated sludge performance and efficiency (Metcalf and Eddy, 2003), which have to be evaluated and compared at the early redesign stage. One of the options is the use of Conceptual Design, which involves a sequence of decisions (issues) about technologies, equipment, dimensions, configuration and operating conditions to develop a process. Conceptual design problems are complex and ill-defined because there are a large number of potential solutions to consider for a certain decision. To reduce the solution space. Hierarchical Decision Process (HDP) was proposed (Douglas, 1988; Smith, 1995). This approach breaks down the conceptual design into several tasks providing an easier way to analyse and evaluate them. Thus, the design process starts with the resolution of all the issues related to the core section of the plant (reactor) and then the design procedure is moved outward by adding the separation and the recirculation sections until a comprehensive design of the plant is achieved.
788 The aim of this paper is to show the benefits of applying the HDP in the redesign of a BNR plant (Biological Nitrogen Removal) to achieve phosphorus removal. The problem of the evaluation procedure is solved applying a multi-criteria method (Flores et al., 2004).
2. Methodology This section describes the application of the HDP in the re-design of activated sludge plants. The HDP fixes the order of all the issues that arise in the redesign process (following the "onion logic" starting the design solving all the issues related to the reactor section). For each issue, a finite set of options that represent the alternative scenarios are generated [A = {Ai,...,Ain}]. Different criteria [X = {Xi,...,Xn}] are used to measure the accompHshment of re-design objectives [OBJ = {OBJi,...,OBJz}]. Weighting factors are assigned to determine the relative importance of the objectives [Wi i = 1,...,z]. Weights are normalized to sum to 1 [ X Wj = 1 ]. All the criteria used [X] are quantified by dynamic simulation, cost estimation and expert knowledge. The quantification of option Aj on criteria Xj is indicated with Xy. Thus, each option under evaluation can be seen as a vector of scores and it can be represented as a n-dimensional performance score profile: [Aj = (xin,..., Xnj)]. Value fimctions [v (Xi)] map the score profiles of all options [v (Aj) = v (xin, ..., Xnj)] into a normalized value from 0 to 1. The 0 and 1 values are associated with the worst (Xi*) and the best (xj) situation whilst a mathematical function is used to evaluate the intermediate effects. The collection of the best [x* = (xi*,...,Xn )] and the worst [x* = (xi* ...,Xn*)] scores for all criteria determine the best [v (x ) = v (xi ,...,Xn ) =1] and the worst profiles [v (x*) = v (xi*,.. .,Xn*) = 0]. With the grey scale evaluation (Copp, 2003), for every criterion (Xj) shades from 0 % to 100 % black are associated with the best (xi) and the worst (xj*) criteria values respectively. Grey intensity of the other options is determined by linear interpolation between these values. Finally, a weighted sum (see eq. 1) is made to obtain a single value for each option. The weighted sum is calculated by adding the product of each normalized criterion v (x^) by its corresponding weight (Wi). The options are ranked according to the score obtained. n
v(Aj) = v(Xij,...,Xnj) = Xwi-v(Xij)
(1)
i=l
The option with the highest score is the recommended, but the final decision rests on the designer, mainly in those cases where the relative differences between the different options is not significant. The design starts at the reactor because it is the most important section of the whole process. It is important to note that the reactor design dictates some of the basic decisions referred to the separation and the recycle. Once all the issues related to the reactor section are evaluated, the process re-design is moved outward evaluating the subsequent layers related to the separation and recycle section
789
3. Case-Study 3.1 Redesign procedure The HDP methodology is applied to redesign the benchmark denitrifying plant (Copp, 2003) to achieve simultaneous carbon, nitrogen a phosphorus removal. The benchmark plant is a modified Ludzack Ettinger configuration with five reactors in series (tanks 1 and 2 are anoxic with a total volume of 2000 m^, while tanks 3, 4 and 5 are aerobic with a total volume of 3999 m^) linked with an internal recirculation from the 3"^^ aerobic tank to the 1^* anoxic tank, a 10-layer secondary settling tank (with a total volume of 6000 m^) and two PI control loops. The first loop controls the dissolved oxygen in the 3*^^ aerobic tank by the manipulation of the aeration flow, and the second loop controls the nitrate level in the 2^^ anoxic tank by manipulating the internal recycle flowrate. The removal of phosphorus from wastewater involves the incorporation of phosphate into the total suspended solids and the subsequent removal of these solids. Phosphorus can be incorporated in to either biological solids (microorganisms) or chemical precipitates. The first issue in the hierarchy of decisions is related to the type of phosphorus removal in the reactor section. Three options are evaluated: (Ai) biological phosphorus removal, considering a preliminary 1250 m^ anaerobic tank (hydraulic residence time of 1.6 hours); (A2) chemical phosphorus precipitation with iron salts in the 3^^^ aerobic reactor, with a constant dosage of 0.75 m^-day"^ (750 kg-day"^), and (A3) hybrid solution with partial biological phosphorus removal (a 750 m^ preliminary anaerobic tank) enhanced with chemical precipitation in the 3^^ aerobic reactor (constant dosage of 0.75 m^-day"^). Re-design objectives are: maximize effluent quality (OBJi); minimize effluent quality violations (OBJ2); minimize construction and operational costs (OBJ3) and maximize control performance (OBJ4). In this case study we assume equal importance for all the re-design objectives, and thus Wi= 0.25 (i = 1 to 4). A set of criteria is proposed to measure the achievement of these objectives. To measure the accomplishment of OBJi one single criterion is proposed (Xi). This criterion is defined as the reduction percentage of the wastewater contaminant load entering the plant (eq 2). Xi relates the effluent (EQ) to the influent (IQ) quality index (See Gemaey et al., 2004 for details about IQ and EQ quantification).
X,=iQzEQ,oo ^
IQ
(2) ^ ^
The effluent quality violation criteria (X2, X3, X4, X5 and X6) are used to measure the accomplishment of OBJ2 and reflect the percentage of time that the effluent concentration of the pollutant exceeds the effluent quality limits (91/271/EEC) during the evaluation period (1 week). The limits for these calculations were: Cp.tot= 2 g-m"^ (X2); CN-tot= 15 g-m-^ (X3); CBOD= 25 g-m-^(X4), CCOD= 125 g-m'^ (X5) and CTSS= 35
There are two criteria to measure the accomplishment of OBJ3, both related to the minimization of the costs: X7 (construction costs) and Xg (operation and maintenance costs). X7J is estimated based on the algorithms to design and evaluate wastewater treatment plants (Hydromantis, 2004b), X7 2 is retrieved from expert knowledge and X7 3
790 is a combination of both. Xg is calculated according to the operating cost performance index (Vanrolleghem and Gillot, 2002), as stated by eq. 3. ^ 8 = aEQ -EQ 4- a AE -AE + apE -PE + a^i^g 'Psidg + OCME 'ME + apE 'FE
(3)
EQ is the effluent quality index; AE, PE and ME represents the aeration, pumping and mixing energy rates (kW-h-day"^), respectively. Psidg is the sludge production rate (kg-day"^) and FE is the quantity of chemicals (m^-day"^). The equations to calculate AE, PE, ME and Psidg can be found in Copp 2003. FE is calculated integrating the quantity of iron salts during the evaluation period. The aj coefficients in eq3 are the operating costs weighting factors and represent yearly operating costs. The values used in the simulations reported in this paper are: aEQ = 50(€-year'^)-EQ'^; aAE =CCPE = aME =25(€-year->(kW-h-day^); asidg= 75(€-year->(kgTSS-day^)-^ and aFE= 190(€-year^)-(m^ Fe(0H)3-day"Y^ All the aj values are obtained from literature (Vanrolleghem and Gillot, 2002), butapE that is estimated For OBJ4 a control performance index, measured with the ISE (Integral Squared Error) is used. The controlled variable is the nitrate concentration in the 2"^ anoxic tank (^OBSERVED), and the deviation from the setpoint (ZSETPOINT =1 g N-NOs'-m"^) during the evaluation time (tp-to) is integrated to obtain X9. This index is represented in eq. 4. X 9 = Je dt = J(Zsgjpojj^j-ZQgggj^ygP)) to
dt
(4)
to
Criteria Xi, X2, X3, X4, X5, X6, Xg and X9 are quantified by dynamic simulation with the commercial software GPS-X (Hydromantis 2004a). The IWA's Activated Sludge Model# 2d was chosen as the biological process model (Henze et al. 1999) and the double-exponential settling velocity function of Takacs et al. (1991) was chosen as a fair representation of the settling process. Default values at 15^C for kinetic and stoichometric parameters are used for the simulations except for phosphorus precipitation kinetics that were adjusted (Gemaey et al., 2002). CAPDETworks (Hydromantis 2004b) software package provides the information for X71 and partially for, X7,3
Once the criteria are quantified we obtain the score profile for each evaluation option. Thus for the option A, its score profile is Ai = (75.59, 100, 87.92; 0, 0, 0, 67•10^ 1.0110^ 1.08), for the option A2 its score profile is A2 = (79.04, 69.84, 100, 0, 0, 0, 3000, 9.50-10^ 1.42) and finally for A3 its score profile is A3 = (80.94; 72.71, 97.92, 0, 0,0,42-10\ 9.24-10^ 0.85). The extreme profiles (based on expert judgement) for the criteria used in this case study are: v (x)* = (xj = 100, X2 = 0, X3= 0, X4 = 0, X5 = 0, X6 = 0, xy = 0; xg =7.00.105; X9 = 0) = V (xi*,..., X9*) = 1 and V (x)* = (xi = 0, X2 = 100, X3 = 100, X4 = 100, X5 = 100, X6 = 100; X7 = 1.00.10^ xg =1.00.10^ X9 = 1.5) = v (xi*,..., X9*) = 0. Then a linear model between these extreme values is adjusted to calculate the intermediate effects, (for criterion Xi has the following value function v (Xi) = 0.01-Xi). With the grey scale evaluation (Table 1) we can observe that Ai is the option that achieves the highest percentage of nitrogen removal (see the normalized value v (X31) of X3,i) but this fact is poorly reflected in the final decision. On the other hand A2 has the lowest construction costs (see v (xy 2)) and the highest percentage of phosphorus
791 removal (see in v (x2,2)). A3 gets the better values of effluent quality, control performance and operational costs as can be seen in v (xi,3), v (x8,i) and v (XQJ) being A3 the option that better achieves objectives 1 (OBJi) and 4 (OBJ4). Note that for this case study, criteria X4, Xs and X6 are not useful to discriminate the alternative designs. Table 1 .Normalized criteria, grey scale evaluation and weighted sums.
OBJ2
V (X4,j) V (X5,j) V (X6,j)
Zwi.v(Xij)
1.00 1.00 1.00
1.00 1.00 1.00
1.00 1.00 1.00
0.05 0.05
0.46
0.52
0.58
1.00
0.05
i=l
Thlis, according to the re-design objectives defined, Ai and A2 are rejected, being A3 the selected option. Nevertheless, in order to test the robustness of the decision a weight sensitivity analysis is made. 3.2 Sensitivity Analysis The weight sensitivity analysis is made between OBJi, OBJ2 and OBJ3. The weight for OBJ4 rests constant (W4 = 0.25), while the remaining 0.75 (as mentioned above, the sum of all the weights has to be 1) is distributed between the weights of OBJi (wi), OBJ2 (W2) and OBJ3 (W3). The weighted sum for the three competing options is recalculated to obtain a rank of the final scores. The same procedure is applied for a second sensitivity analysis but in this case the analysis is made between W2, W3 and W4. 1,0
1.0
0,8
0,8
A2
0,6 CM
) ) ) ) )
O O O O O
FEASIBLE REGION
O OO O OO O O OO
f • • 0,6 f • • •
• ••• •
> o o o o o o 0,4 ) O O O O O O O
O O O 0
) O O ®OCtO O O ) O O 9 O O C^O O O ) O O <8bO O O 9 O O O
0,2 ) o o o f i o 0 0 0 0 0 0 0,0
> • • • aooooooooooo 0,2
0,4
0,6 Wi
O • • • O O • • O # 0«€) 0 9 0 0
• • • • • • Ck^ • •
0,2 < 0 0 0 0 0 OT) O • •
»OOOOCi(i€)00000 ( • O O O O O O O O O O O O • • • O O O O O O O O O O O O
0,0
A3 FEASIBLE REGION
0,8
1,0
o o 0^0 o g o o • • 0 0 0 oT) (i-<5 o o o • • oooooooooooo* ooooooooooooo* 0,2
0,4
0,6
1,0
w.
Figure 1. Sensitivity analysis a) varying simultaneously Wj, W2 andws and b) varying simultaneously w2, w3 and w4.
792 As shown in Figure 1, for each (x,y) pair, the option with the best score is plotted (Note that the parameter z is not represented but it depends on x and y by the restriction x + y + z = 0.75). From the results generated by both sensitivity analyses we conclude that A3 is the most preferred option for a wide range of situations in the feasible region (in this case study the feasible region is considered as ± 0.10 of the weight value and it is represented with a dotted line), compared to A2. Thus the selected option for the first issue will be A3. The redesign procedure will continue evaluating the rest of the issues of the reactor section (point of chemical addition, control strategy for iron dosage...) separation section (increase of settling area...) and finally recirculation section (additional recirculation from the anoxic to anaerobic zone)
4. Conclusions An application of the HDP to re-design activated sludge plants based on a multi-criteria evaluation method has been developed. The multi-criteria method allows the inclusion of different objectives and the HDP reduces the design problem to a set of issues that follow a predefined order (reactor, separation and recirculation). A sensitivity analysis has been applied to test the robustness of the decision between the competing options and it has proved to be a useful tool to support and complement the decision procedure. References Copp J.B, 2003. Respirometry in Control of the Activated Sludge Process: Benchmarking Control Strategies. IWA Scientific and technical Report No. 11. Gemaey K., Mussati M., Yuan Z., Nielsen M.K. and Jorgensen, S.B. 2002. Control Strategy Evaluation for Combined N and P Removal using a Benchmark Wastewater Treatment Plant. 15th IF AC world Congress for automatic control. July 21-26, Barcelona, Spain. Gemaey, K.V. and Jorgensen, S.B. 2004. Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes. Control Eng. Pract., 12, 357-373. Douglas J.M. ,1988. Conceptual design of chemical processes. McGraw-Hill. New York. Flores X., Bonmati A., Poch M., Banares-Alcantara R. and Rodriguez-Roda. 2004. Hierarchical Decision Process combined with Multicriteria Analysis to Support Conceptual Design of Activated Sludge Plants. European Symposium on Computer Aided Process EngineeringH.May 16-19. Lisboa. Portugal. Henze M., Gujer W., Mino T., Matuso T., Wentzel., Marais G.v.R. and Van Loosdrecht. 1999. Activated Sludge Model No 2d. ASM2d.Wat. Sci. Tech. 39.(1), 165-182. Hydromantis Inc. 2004a. GPS-X. Ontario, Canada. Hydromantis Inc 2004b. CAPDETworks. Ontario, Canada. Metcalf & Eddy. 2003. Wastewater Engineering : Treatment, Disposal and Reuse. Mc-Graw-Hill Book Company: New York. Smith R. 1995. Chemical Process design.. Mc-Graw-Hill. Takacs, I., Patry, G.G. and Nolasco, D. 1991. A dynamic model of the clarification thickening process. Wat. Res., 25(10), 1263-1271. Vanrolleghem, P. and Gillot, S. (2002). Robustness and economic measures as control benchmark performance criteria. Wat. Sci. Tech., 45(4/5), 117-126.
Acknowledgements The authors gratefully acknowledge financial support from Spanish "Ministerio de Ciencia y Tecnologia" under the project DPI2003-09392-C02-01.
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Hierarchical Decision Approach: Key to Activated Sludge Process Redesign Xavier Flores^, Manel Poch^, Ignasi Rodriguez-Roda^, Rene Banares-Alcantara^ and Laureano Jimenez'^ ^Laboratori d'Enginyeria Quimica i Ambiental, Universitat de Girona Campus Montilivi s/n. 17071 Girona, Spain ^Department of Engineering Science, University of Oxford Parks Roads, Oxford 0X1 3PJ, U.K. '^Departament d'Enginyeria Quimica i MetaMurgica, Universitat de Barcelona Marti i Franques 1, 08028 Barcelona, Spain
Abstract Hierarchical Decision Process has been applied to redesign a wastewater treatment plant to achieve phosphorus removal The Hierarchical Decision Process suggests the order in which the decisions should be taken during the development of the new process, starting with the design of the reactor due to the strong interaction with the rest of the process. The redesign task is formulated as a multicriteria problem and considers simultaneously environmental, economical, legal and technical aspects. Finally, a sensitivity analysis is made to test the robustness and support the decision making procedure. Keywords: Activated sludge. Conceptual design. Nutrient removal. Hierarchical decision process, Multicriteria analysis.
1. Introduction The application of the European Directive (91/271/EEC) for urban wastewater treatment in sensitive areas requires a higher efficiency in nutrient removal from activated sludge plants. To achieve the strict phosphorus and nitrogen discharge limits implies a redesign of several units. Literature describes different options to enhance activated sludge performance and efficiency (Metcalf and Eddy, 2003), which have to be evaluated and compared at the early redesign stage. One of the options is the use of Conceptual Design, which involves a sequence of decisions (issues) about technologies, equipment, dimensions, configuration and operating conditions to develop a process. Conceptual design problems are complex and ill-defined because there are a large number of potential solutions to consider for a certain decision. To reduce the solution space. Hierarchical Decision Process (HDP) was proposed (Douglas, 1988; Smith, 1995). This approach breaks down the conceptual design into several tasks providing an easier way to analyse and evaluate them. Thus, the design process starts with the resolution of all the issues related to the core section of the plant (reactor) and then the design procedure is moved outward by adding the separation and the recirculation sections until a comprehensive design of the plant is achieved.
788 The aim of this paper is to show the benefits of applying the HDP in the redesign of a BNR plant (Biological Nitrogen Removal) to achieve phosphorus removal. The problem of the evaluation procedure is solved applying a multi-criteria method (Flores et al., 2004).
2. Methodology This section describes the application of the HDP in the re-design of activated sludge plants. The HDP fixes the order of all the issues that arise in the redesign process (following the "onion logic" starting the design solving all the issues related to the reactor section). For each issue, a finite set of options that represent the alternative scenarios are generated [A = {Ai,...,Ain}]. Different criteria [X = {Xi,...,Xn}] are used to measure the accompHshment of re-design objectives [OBJ = {OBJi,...,OBJz}]. Weighting factors are assigned to determine the relative importance of the objectives [Wi i = 1,...,z]. Weights are normalized to sum to 1 [ X Wj = 1 ]. All the criteria used [X] are quantified by dynamic simulation, cost estimation and expert knowledge. The quantification of option Aj on criteria Xj is indicated with Xy. Thus, each option under evaluation can be seen as a vector of scores and it can be represented as a n-dimensional performance score profile: [Aj = (xin,..., Xnj)]. Value fimctions [v (Xi)] map the score profiles of all options [v (Aj) = v (xin, ..., Xnj)] into a normalized value from 0 to 1. The 0 and 1 values are associated with the worst (Xi*) and the best (xj) situation whilst a mathematical function is used to evaluate the intermediate effects. The collection of the best [x* = (xi*,...,Xn )] and the worst [x* = (xi* ...,Xn*)] scores for all criteria determine the best [v (x ) = v (xi ,...,Xn ) =1] and the worst profiles [v (x*) = v (xi*,.. .,Xn*) = 0]. With the grey scale evaluation (Copp, 2003), for every criterion (Xj) shades from 0 % to 100 % black are associated with the best (xi) and the worst (xj*) criteria values respectively. Grey intensity of the other options is determined by linear interpolation between these values. Finally, a weighted sum (see eq. 1) is made to obtain a single value for each option. The weighted sum is calculated by adding the product of each normalized criterion v (x^) by its corresponding weight (Wi). The options are ranked according to the score obtained. n
v(Aj) = v(Xij,...,Xnj) = Xwi-v(Xij)
(1)
i=l
The option with the highest score is the recommended, but the final decision rests on the designer, mainly in those cases where the relative differences between the different options is not significant. The design starts at the reactor because it is the most important section of the whole process. It is important to note that the reactor design dictates some of the basic decisions referred to the separation and the recycle. Once all the issues related to the reactor section are evaluated, the process re-design is moved outward evaluating the subsequent layers related to the separation and recycle section
789
3. Case-Study 3.1 Redesign procedure The HDP methodology is applied to redesign the benchmark denitrifying plant (Copp, 2003) to achieve simultaneous carbon, nitrogen a phosphorus removal. The benchmark plant is a modified Ludzack Ettinger configuration with five reactors in series (tanks 1 and 2 are anoxic with a total volume of 2000 m^, while tanks 3, 4 and 5 are aerobic with a total volume of 3999 m^) linked with an internal recirculation from the 3"^^ aerobic tank to the 1^* anoxic tank, a 10-layer secondary settling tank (with a total volume of 6000 m^) and two PI control loops. The first loop controls the dissolved oxygen in the 3*^^ aerobic tank by the manipulation of the aeration flow, and the second loop controls the nitrate level in the 2^^ anoxic tank by manipulating the internal recycle flowrate. The removal of phosphorus from wastewater involves the incorporation of phosphate into the total suspended solids and the subsequent removal of these solids. Phosphorus can be incorporated in to either biological solids (microorganisms) or chemical precipitates. The first issue in the hierarchy of decisions is related to the type of phosphorus removal in the reactor section. Three options are evaluated: (Ai) biological phosphorus removal, considering a preliminary 1250 m^ anaerobic tank (hydraulic residence time of 1.6 hours); (A2) chemical phosphorus precipitation with iron salts in the 3^^^ aerobic reactor, with a constant dosage of 0.75 m^-day"^ (750 kg-day"^), and (A3) hybrid solution with partial biological phosphorus removal (a 750 m^ preliminary anaerobic tank) enhanced with chemical precipitation in the 3^^ aerobic reactor (constant dosage of 0.75 m^-day"^). Re-design objectives are: maximize effluent quality (OBJi); minimize effluent quality violations (OBJ2); minimize construction and operational costs (OBJ3) and maximize control performance (OBJ4). In this case study we assume equal importance for all the re-design objectives, and thus Wi= 0.25 (i = 1 to 4). A set of criteria is proposed to measure the achievement of these objectives. To measure the accomplishment of OBJi one single criterion is proposed (Xi). This criterion is defined as the reduction percentage of the wastewater contaminant load entering the plant (eq 2). Xi relates the effluent (EQ) to the influent (IQ) quality index (See Gemaey et al., 2004 for details about IQ and EQ quantification).
X,=iQzEQ,oo ^
IQ
(2) ^ ^
The effluent quality violation criteria (X2, X3, X4, X5 and X6) are used to measure the accomplishment of OBJ2 and reflect the percentage of time that the effluent concentration of the pollutant exceeds the effluent quality limits (91/271/EEC) during the evaluation period (1 week). The limits for these calculations were: Cp.tot= 2 g-m"^ (X2); CN-tot= 15 g-m-^ (X3); CBOD= 25 g-m-^(X4), CCOD= 125 g-m'^ (X5) and CTSS= 35
There are two criteria to measure the accomplishment of OBJ3, both related to the minimization of the costs: X7 (construction costs) and Xg (operation and maintenance costs). X7J is estimated based on the algorithms to design and evaluate wastewater treatment plants (Hydromantis, 2004b), X7 2 is retrieved from expert knowledge and X7 3
790 is a combination of both. Xg is calculated according to the operating cost performance index (Vanrolleghem and Gillot, 2002), as stated by eq. 3. ^ 8 = aEQ -EQ 4- a AE -AE + apE -PE + a^i^g 'Psidg + OCME 'ME + apE 'FE
(3)
EQ is the effluent quality index; AE, PE and ME represents the aeration, pumping and mixing energy rates (kW-h-day"^), respectively. Psidg is the sludge production rate (kg-day"^) and FE is the quantity of chemicals (m^-day"^). The equations to calculate AE, PE, ME and Psidg can be found in Copp 2003. FE is calculated integrating the quantity of iron salts during the evaluation period. The aj coefficients in eq3 are the operating costs weighting factors and represent yearly operating costs. The values used in the simulations reported in this paper are: aEQ = 50(€-year'^)-EQ'^; aAE =CCPE = aME =25(€-year->(kW-h-day^); asidg= 75(€-year->(kgTSS-day^)-^ and aFE= 190(€-year^)-(m^ Fe(0H)3-day"Y^ All the aj values are obtained from literature (Vanrolleghem and Gillot, 2002), butapE that is estimated For OBJ4 a control performance index, measured with the ISE (Integral Squared Error) is used. The controlled variable is the nitrate concentration in the 2"^ anoxic tank (^OBSERVED), and the deviation from the setpoint (ZSETPOINT =1 g N-NOs'-m"^) during the evaluation time (tp-to) is integrated to obtain X9. This index is represented in eq. 4. X 9 = Je dt = J(Zsgjpojj^j-ZQgggj^ygP)) to
dt
(4)
to
Criteria Xi, X2, X3, X4, X5, X6, Xg and X9 are quantified by dynamic simulation with the commercial software GPS-X (Hydromantis 2004a). The IWA's Activated Sludge Model# 2d was chosen as the biological process model (Henze et al. 1999) and the double-exponential settling velocity function of Takacs et al. (1991) was chosen as a fair representation of the settling process. Default values at 15^C for kinetic and stoichometric parameters are used for the simulations except for phosphorus precipitation kinetics that were adjusted (Gemaey et al., 2002). CAPDETworks (Hydromantis 2004b) software package provides the information for X71 and partially for, X7,3
Once the criteria are quantified we obtain the score profile for each evaluation option. Thus for the option A, its score profile is Ai = (75.59, 100, 87.92; 0, 0, 0, 67•10^ 1.0110^ 1.08), for the option A2 its score profile is A2 = (79.04, 69.84, 100, 0, 0, 0, 3000, 9.50-10^ 1.42) and finally for A3 its score profile is A3 = (80.94; 72.71, 97.92, 0, 0,0,42-10\ 9.24-10^ 0.85). The extreme profiles (based on expert judgement) for the criteria used in this case study are: v (x)* = (xj = 100, X2 = 0, X3= 0, X4 = 0, X5 = 0, X6 = 0, xy = 0; xg =7.00.105; X9 = 0) = V (xi*,..., X9*) = 1 and V (x)* = (xi = 0, X2 = 100, X3 = 100, X4 = 100, X5 = 100, X6 = 100; X7 = 1.00.10^ xg =1.00.10^ X9 = 1.5) = v (xi*,..., X9*) = 0. Then a linear model between these extreme values is adjusted to calculate the intermediate effects, (for criterion Xi has the following value function v (Xi) = 0.01-Xi). With the grey scale evaluation (Table 1) we can observe that Ai is the option that achieves the highest percentage of nitrogen removal (see the normalized value v (X31) of X3,i) but this fact is poorly reflected in the final decision. On the other hand A2 has the lowest construction costs (see v (xy 2)) and the highest percentage of phosphorus
791 removal (see in v (x2,2)). A3 gets the better values of effluent quality, control performance and operational costs as can be seen in v (xi,3), v (x8,i) and v (XQJ) being A3 the option that better achieves objectives 1 (OBJi) and 4 (OBJ4). Note that for this case study, criteria X4, Xs and X6 are not useful to discriminate the alternative designs. Table 1 .Normalized criteria, grey scale evaluation and weighted sums.
OBJ2
V (X4,j) V (X5,j) V (X6,j)
Zwi.v(Xij)
1.00 1.00 1.00
1.00 1.00 1.00
1.00 1.00 1.00
0.05 0.05
0.46
0.52
0.58
1.00
0.05
i=l
Thlis, according to the re-design objectives defined, Ai and A2 are rejected, being A3 the selected option. Nevertheless, in order to test the robustness of the decision a weight sensitivity analysis is made. 3.2 Sensitivity Analysis The weight sensitivity analysis is made between OBJi, OBJ2 and OBJ3. The weight for OBJ4 rests constant (W4 = 0.25), while the remaining 0.75 (as mentioned above, the sum of all the weights has to be 1) is distributed between the weights of OBJi (wi), OBJ2 (W2) and OBJ3 (W3). The weighted sum for the three competing options is recalculated to obtain a rank of the final scores. The same procedure is applied for a second sensitivity analysis but in this case the analysis is made between W2, W3 and W4. 1,0
1.0
0,8
0,8
A2
0,6 CM
) ) ) ) )
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Figure 1. Sensitivity analysis a) varying simultaneously Wj, W2 andws and b) varying simultaneously w2, w3 and w4.
792 As shown in Figure 1, for each (x,y) pair, the option with the best score is plotted (Note that the parameter z is not represented but it depends on x and y by the restriction x + y + z = 0.75). From the results generated by both sensitivity analyses we conclude that A3 is the most preferred option for a wide range of situations in the feasible region (in this case study the feasible region is considered as ± 0.10 of the weight value and it is represented with a dotted line), compared to A2. Thus the selected option for the first issue will be A3. The redesign procedure will continue evaluating the rest of the issues of the reactor section (point of chemical addition, control strategy for iron dosage...) separation section (increase of settling area...) and finally recirculation section (additional recirculation from the anoxic to anaerobic zone)
4. Conclusions An application of the HDP to re-design activated sludge plants based on a multi-criteria evaluation method has been developed. The multi-criteria method allows the inclusion of different objectives and the HDP reduces the design problem to a set of issues that follow a predefined order (reactor, separation and recirculation). A sensitivity analysis has been applied to test the robustness of the decision between the competing options and it has proved to be a useful tool to support and complement the decision procedure. References Copp J.B, 2003. Respirometry in Control of the Activated Sludge Process: Benchmarking Control Strategies. IWA Scientific and technical Report No. 11. Gemaey K., Mussati M., Yuan Z., Nielsen M.K. and Jorgensen, S.B. 2002. Control Strategy Evaluation for Combined N and P Removal using a Benchmark Wastewater Treatment Plant. 15th IF AC world Congress for automatic control. July 21-26, Barcelona, Spain. Gemaey, K.V. and Jorgensen, S.B. 2004. Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes. Control Eng. Pract., 12, 357-373. Douglas J.M. ,1988. Conceptual design of chemical processes. McGraw-Hill. New York. Flores X., Bonmati A., Poch M., Banares-Alcantara R. and Rodriguez-Roda. 2004. Hierarchical Decision Process combined with Multicriteria Analysis to Support Conceptual Design of Activated Sludge Plants. European Symposium on Computer Aided Process EngineeringH.May 16-19. Lisboa. Portugal. Henze M., Gujer W., Mino T., Matuso T., Wentzel., Marais G.v.R. and Van Loosdrecht. 1999. Activated Sludge Model No 2d. ASM2d.Wat. Sci. Tech. 39.(1), 165-182. Hydromantis Inc. 2004a. GPS-X. Ontario, Canada. Hydromantis Inc 2004b. CAPDETworks. Ontario, Canada. Metcalf & Eddy. 2003. Wastewater Engineering : Treatment, Disposal and Reuse. Mc-Graw-Hill Book Company: New York. Smith R. 1995. Chemical Process design.. Mc-Graw-Hill. Takacs, I., Patry, G.G. and Nolasco, D. 1991. A dynamic model of the clarification thickening process. Wat. Res., 25(10), 1263-1271. Vanrolleghem, P. and Gillot, S. (2002). Robustness and economic measures as control benchmark performance criteria. Wat. Sci. Tech., 45(4/5), 117-126.
Acknowledgements The authors gratefully acknowledge financial support from Spanish "Ministerio de Ciencia y Tecnologia" under the project DPI2003-09392-C02-01.